This paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a Hierarchical Hidden Markov Model (HHMM). This model makes use...
Our goal is to use natural language processing to identify deceptive and nondeceptive passages in transcribed narratives. We begin by motivating an analysis of language-based dece...
Joan Bachenko, Eileen Fitzpatrick, Michael Schonwe...
Distributional measures of lexical similarity and kernel methods for classification are well-known tools in Natural Language Processing. We bring these two methods together by int...
In Japanese dependency parsing, Kudo's relative preference-based method (Kudo and Matsumoto, 2005) outperforms both deterministic and probabilistic CKY-based parsing methods....
State-of-the-art statistical parsing models applied to free word-order languages tend to underperform compared to, e.g., parsing English. Constituency-based models often fail to c...
In this paper, we work on extending a Chinese thesaurus with words distinctly used in various Chinese communities. The acquisition and classification of such region-specific lexic...
This paper studies sentiment analysis from the user-generated content on the Web. In particular, it focuses on mining opinions from comparative sentences, i.e., to determine which...
This paper presents a method of retrieving bilingual collocations of a verb and its objective noun from cross-lingual documents with similar contents. Relevant documents are obtai...
In this paper we present a study on the interpretation of weekday names in texts. Our algorithm for assigning a date to a weekday name achieves 95.91% accuracy on a test data set ...
Matching coreferent named entities without prior knowledge requires good similarity measures. Soft-TFIDF is a fine-grained measure which performs well in this task. We propose to ...